Physical rig testing of a vehicle is often undertaken to obtain experimental data that can be used to ensure a mathematical model is an accurate representation of the vehicle under study. Kinematics and Compliance (K&C) testing is often used for this purpose. The relationship between the hard point locations and compliance parameters, and K&C characteristics of a suspension system is complex, and so automating the process to correlate the model to the test data can make the exercise easier, faster and more accurate than hand tuning the model. In this work, such a process is developed. First, the model parameters are adjusted, next a simulation is run, before the results are read and post processed. This automation processed is used in conjunction with an optimization procedure to carry out the K&C correlation.MATLAB scripts are created to modify the model parameters, run simulations and read the results so that MATLAB optimization algorithms can be used to identify the most appropriate suspension parameter values.Once the necessary MATLAB scripts were completed, the MATLAB patternsearch direct search constrained optimization algorithm was used to successfully obtain an acceptable correlation with experimental K&C test data. The desired kinematic properties were achieved by adjusting the hard point locations, and the desired compliance characteristics were achieved by adjusting the bushing stiffnesses.While the method used in this work was applied to a K&C correlation procedure, this type of automation of Adams/Car is useful in numerous situations such as design optimization or specific batch runs that cannot be handled using the built in Adams tools.